Many applications for language models, including chat bots, search engines, text summarization systems, question answer systems, machine translation systems, etc., rely on open-ended text generation. As such, we argue that it is important to quantify how societally relevant aspects of open-ended text generation — relevancy, accuracy, safety, and even creative expression (see Appendix REF  for a discussion on AI generated poetry) — scale with model size. It will also be important to develop techniques that can improve the factual accuracy of the results of AI models, as described in e.g., [1]}, and to make the outputs of models more appropriate and less likely to display harmful biases [2]}.
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